An algorithm for the robust estimation of the COVID-19 pandemic’s population by considering undetected individuals.
作者：
Highlights：
• Utilization of an algebraic differential approach and a modified, augmented system (immersion) of the A-SIR model.
• Development of a non redundant and model-free observer. No reconstruction of known variables. Full model is not required
• The estimation algorithm is robust. Asymptotically stable (noise-free) or ultimate uniformly bounded (noise).
• .
• Reconstruction of the variables of interest by means of integration processes (Liouvillian system).
• Validation of the methodology with real data reported by the Mexican Government.
摘要
•Utilization of an algebraic differential approach and a modified, augmented system (immersion) of the A-SIR model.•Development of a non redundant and model-free observer. No reconstruction of known variables. Full model is not required•The estimation algorithm is robust. Asymptotically stable (noise-free) or ultimate uniformly bounded (noise).•.•Reconstruction of the variables of interest by means of integration processes (Liouvillian system).•Validation of the methodology with real data reported by the Mexican Government.
论文关键词：COVID-19 pandemic,A-SIR model,Asymptomatic individuals estimation,Model-free estimation algorithm
论文评审过程：Received 20 November 2020, Revised 19 March 2021, Accepted 5 April 2021, Available online 8 April 2021, Version of Record 18 April 2021.
论文官网地址：https://doi.org/10.1016/j.amc.2021.126273
